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Beyond Burden: A Health Capability Gap Metric as the Foundation for a GBD+ Framework

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05 August 2026

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06 August 2026

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Abstract
The Global Burden of Disease study quantifies health loss through disability-adjusted life years (DALYs) but captures only the outcome dimension of health capability. The paper argues that adding a structurally defined Health Resource Constraint component to DALYs would reveal a health capability deficit beyond what DALYs alone capture and distinguish realised outcome losses from the structural conditions that produce or precede them. The resulting Health Capability Gap constitutes a GBD+ metric: an extension of burden measurement into capability measurement. This reframing would align global health monitoring with the capability-based and rights-based commitments of the Sustainable Development Goals, Universal Health Coverage, and the WHO Constitution.
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Background
The Global Burden of Disease study marked a turning point in global health measurement. By bringing together mortality, morbidity, and risk factor attribution within a common empirical architecture, it made possible a systematic comparison of health loss across populations and over time [1]. Yet, as global health policy has moved from measuring disease burden toward universal health coverage and sustainable development, the evaluative question and space have also changed [2,3]. It is no longer sufficient to ask only how much health was lost. It is also necessary to ask whether individuals had the conditions required to avoid that loss.
Yet the GBD measures only one dimension of health: the outcome of disease and premature death. DALYs capture the outcome dimension of health capability: years cut short by premature death and years lived with illness or disability. They do not, however, measure whether people had the resources, institutional arrangements, health system settings and social freedoms required to realise health in the first place. Some of these conditions feature in the GBD architecture only as risk factors to which burden is retrospectively attributed. The conditions determining whether health capability can be realised are not counted as a distinct component of the capability gap and, in effect, are invisible to the metric.
This distinction is not merely technical; it has implications for health equity, measuring health gaps, and service delivery. Sen’s capability approach holds that human development should be evaluated not only by achieved outcomes but also by the real freedoms individuals have to lead the lives they value [4]. Applied to health, this means that disease burden and health capability deficits are not the same. A person who dies young due to inadequate access to healthcare and a person who dies at the same age due to an unavoidable genetic condition may produce identical DALYs, yet they represent different societal capability gaps with divergent policy implications.
The paper makes three contributions: it distinguishes realised health loss from health capability deprivation; it identifies Health Resource Constraints as a distinct measurement domain rather than only as retrospective risk factors; and it sketches how a GBD+ framework could extend burden measurement while preserving the life-course accounting logic of the GBD.
A Conceptual Sketch of the Health Capability Gap
The Health Capability Gap (HCG) is conceived as the aggregate life-course shortfall between full health capability and realised health capability, measured through health outcomes. This is comparable to the concept of DALYs in the GBD framework: DALYs are a health-loss measure that captures only achieved outcomes, whereas HCG is a capability-gap measure that captures both health loss and resource gaps. HCG comprises three components that contribute to this shortfall: Health Resource Constraints (HRC), Complete Health Capability Exclusion (CHE), and Partial Health Capability Loss (PHE).
CHE captures the complete loss of life-span capability due to premature mortality (YLLK), while PHE captures partial loss of capability due to illness, disability, or functional limitation (YLDK). Corresponding to YLL in the GBD, (YLLK) reinterprets the same quantity as involuntary exclusion from the life course—the total elimination of the capacity to exercise health-related freedoms—rather than as a demographic shortfall. Corresponding to YLD in GBD, (YLDK) reinterprets non-fatal health loss as a partial capability constraint: a person nominally alive but unable to realise health capability in full.
To these outcome measures, the HCG framework adds an explicit resource-related component, HRC, measured by years of health capability lost due to the structural shortfall in the conditions required to realise health capability (YLSK). These include access to healthcare, food security, adequate housing, safe water and sanitation, and protection from adverse climate and environmental conditions. Climate change degrades these enabling conditions through extreme heat, flooding, drought, displacement, and climate-sensitive shifts in disease ecology [6], even before a disease or death is recorded, thereby constituting a present structural deficit independent of any recorded outcome. Together, the three components (YLLK, YLDK, and YLSK) define HCG, and extend the GBD from an outcome-only account of health loss to a GBD+ account of structurally constrained health freedom. To put it metaphorically, if DALYs are the visible tip of health loss already recorded, HRC is the submerged mass of structural deficits that have yet to surface as disease—and the HCG is the entire iceberg.
Measurement Approach: Years Lost Due to Structural Constraints (YLSK)
The measurement of YLLK and YLDK follows the GBD architecture, with the only difference being their interpretation as measures of aspects of the health capability gap. On the other hand, years of health capability lost due to structural constraints (YLSK) can be estimated by identifying deprivation states that reduce the real freedom to achieve health, such as the absence of essential healthcare, severe food insecurity, unsafe sanitation, inadequate housing, or climate vulnerability.
Three points need to be emphasised. First, HRCs, measured through YLSK, are not the same as GBD risk factor attribution. Risk factors explain what proportion of past DALYs is attributable to upstream exposures. HRCs are prospective capability deficits: present shortfalls that may exist before any recorded disease event. A child without access to primary healthcare or a community exposed to recurrent flooding faces such a deficit before a single DALY is counted. Second, these states are not sequelae in the strict GBD sense, because they are not downstream consequences of disease or injury. They are structural capability-deprivation states. Third, not every exposure is automatically treated as a capability constraint. Behavioural risks require careful treatment because they raise questions about voluntariness, constrained preference formation, addiction, and commercial influence. However, they can be operationalised analogously to sequelae: each state can be assigned a disutility weight, overlapping states can be combined using a multiplicative function, and the result can be accumulated over person-years to estimate healthy capability-years lost.
Each deprivation state can be assigned a Health Resource Constraint disutility weight between 0 and 1, representing the average reduction in health capability associated with that deficit, independent of any pre-existing disease. These weights are conceptually analogous to disability weights in the GBD, which quantify the severity of non-fatal health states for calculating years lived with disability [7]. The difference is that disability weights capture health loss from disease or injury sequelae, whereas health resource constraint disutility weights capture loss of health capability arising from structural deprivation.
When individuals experience multiple deficits, combined disutility weights can be calculated using the multiplicative approach already used in the GBD for multiple health conditions. Multiplying age-specific person-years exposed to structural deprivation by the average combined disutility weight yields the HRC component, expressed as years of health capability lost due to structural constraint.
Formally, the Health Capability Gap can be expressed as HCG = YLLᴷ + YLDᴷ + YLSᴷ. The first two components correspond to DALYs, reinterpreted as complete and partial losses of health capability. The third component, YLSᴷ, captures years of health capability lost due to structural constraints, adjusted to avoid double-counting losses already reflected in morbidity or mortality. Thus, HCG extends DALYs by accounting for residual structural capability loss that has not yet manifested as a recorded disease event and that outcome-only metrics do not capture.
A complementary summary metric, Capability-Adjusted Life Expectancy (CALE), captures the expected years of life lived in full health capability under current structural and epidemiological conditions. The measure is related to poverty-free and poverty-adjusted life expectancy, which recognises that survival alone is incomplete when years are lived in poverty [8,9]. The difference is that those measures adjust survival for poverty, whereas CALE/HCG extends the GBD architecture by treating the underlying manifestations (such as severe food insecurity, unsafe sanitation, inadequate housing) as structural constraints among others and by adding health capability loss to the DALY outcome components. A formal representation of the accounting framework and the relationship between CALE and HALE is provided in the Supplementary Appendix.
Implications
Adopting the Health Capability Gap as a GBD+ framework would have four implications. First, it would directly reveal the structural origins of health inequality—including climate vulnerability—that are invisible to outcome metrics. Existing metrics show that populations differ in health outcomes, but do not distinguish outcomes arising from inadequate structural conditions from those arising from less avoidable causes. Settings with high HRC relative to DALYs are those in which the GBD is most likely to severely underestimate health capability deprivation, because the structural component may not yet have produced a disease event. Integrating HRC directly into the measurement of the health capability gap makes it visible and quantifiable, enabling investment in the structural conditions that produce health rather than solely in treating their absence.
Second, it supports the diagnosis of the intervention mix required and sharpens accountability within the health system. The HCG decomposes deprivation into HRC, PHE, and CHE. A system achieving low DALYs through treatment in a structurally deprived setting is performing differently from one that maintains the structural conditions preventing disease. This directs resources toward the dominant source of capability loss: structural investment, where HRC dominates, and clinical scale-up, where CHE or PHE dominate.
Third, it aligns measurement with normative commitments. The Sustainable Development Goals, Universal Health Coverage, and WHO’s constitutional framing of health as a right are articulated in capability or rights-based language, yet monitored largely through outcome-based indicators [2,3,9]. The HCG operationalises what these frameworks actually protect: the conditions under which health capability can be freely realised.
Fourth, it would reposition climate change as a structural determinant of capability. The GBD attributes disease burden to climate-related risks, but it does not count structural losses that precede disease events, such as disrupted food systems, displacement, or loss of secure habitation.
Limitations and Future Directions
The proposed approach is not without limitations. First, there is a risk of double counting. The proposed measurement strategy affects the health capability gap through two pathways: directly through deprivation ((YLSK)) and indirectly through disease outcomes already captured in DALYs ((YLLK + YLDK)). HRC must measure the structural shortfall, net of outcome effects, already in DALYs; simply summing (YLSK) and DALYs would count the same deprivation twice. Hence, HRC must be operationalised, with particular attention to addressing double-counting. This could be done by borrowing the exposure-counterfactual logic of GBD comparative risk assessment, but by treating structural deprivation as an ex ante loss of capability rather than merely as a source of attributable DALYs. Implementation could use mediation analysis, multistate life-table methods, and causal approaches such as sequential g-estimation or inverse probability weighting to separate direct capability loss from disease pathways already counted as YLLK and YLDK.
Second, the multiplicative combination function is a theoretical assumption—deficits may interact synergistically or antagonistically—and sensitivity analyses using alternative combination rules should accompany estimates until empirical data are available. Third, incorporating behavioural risks into measurement requires normative judgement, as not all choices are made under equal conditions. Fourth, Health Resource Constraint disutility weights require validation; GBD disability weights capture average valuations of health states, not necessarily capability losses arising from structural deprivation across institutional contexts. Fifth, as illustrated here, the HCG represents a population-level aggregate and requires distributional extension by income, geography, gender, age, disability, and other axes of inequality.
A further consideration for future research concerns the distributional sensitivity of the aggregate HCG. Because the metric sums all shortfalls from full health capability, a large number of mild capability deficits in a well-resourced population could, in principle, outweigh a smaller number of severe deficits in a highly deprived population. Whether this is judged appropriate depends on the normative framework adopted. Where a sufficientarian [10] standard is preferred—holding that justice requires ensuring everyone reaches a minimally adequate threshold of health capability—future work could develop a distribution-sensitive variant that restricts the HCG to person-years lived or lost below a defined sufficiency threshold, or that weights deficits by their depth below that threshold. This would strengthen the metric’s ability to track severe capability poverty and align it more closely with rights-based monitoring.
Conclusions
The GBD measures what happened to health. The HCG provides a structured way to measure not only realised health loss but also the structural shortfall in the conditions required to achieve health. Explicitly integrating health resource constraints into the measurement of health capability yields a GBD+ metric that complements DALYs wherever structural deprivation exists, and even more so when climate and environmental conditions erode the enabling conditions for health capability. The GBD has built an unparalleled infrastructure for counting what is lost; the HCG extends it into the space that matters equally: the health capability that structural conditions foreclosed before any disease event registered. Grounded in the capability approach, the sufficientarian threshold account, and a disutility-weight framework mirroring the GBD’s own measurement logic, the HCG offers a foundation for global health monitoring that counts not only burden but also freedom.

Appendix A. Mathematical Representation of the Health Capability Gap and Capability-Adjusted Life Expectancy

Appendix A.1. Conceptual Accounting Framework

The Health Capability Gap, denoted H C G , is defined as the aggregate life-course shortfall between full health capability and realised health capability. It comprises three components: complete health capability exclusion, partial health capability loss, and health resource constraint loss. In the paper’s conceptual sketch, these correspond respectively to Y L L ( K ) , Y L D ( K ) , and Y L S ( K ) .
H C G = Y L L ( K ) + Y L D ( K ) + Y L S ( K )
where:
Y L L ( K )
denotes years of life lost interpreted as complete health capability exclusion due to premature mortality;
Y L D ( K )
denotes years lived with disability interpreted as partial health capability loss due to morbidity, disability, or functional limitation;
and:
Y L S ( K )
denotes years of health capability lost due to structural constraints, including deficits in the social, economic, institutional, and environmental conditions required to realise health capability.
The standard DALY identity is:
D A L Y = Y L L + Y L D
In the HCG framework:
Y L L ( K ) Y L L
and:
Y L D ( K ) Y L D
Therefore:
D A L Y = Y L L ( K ) + Y L D ( K )
and:
H C G = D A L Y + Y L S ( K )
provided that Y L S ( K ) is estimated net of structural deprivation already expressed through morbidity or mortality outcomes captured in DALYs.

Appendix A.2. Estimating YLL (K)

Let:
d a , s , c , t
denote deaths in age group a , sex s , cause c , and time period t , and let:
L a
denote the standard remaining life expectancy at age a .
Then:
Y L L ( K ) a , s , c , t = d a , s , c , t L a
and:
Y L L ( K ) = a s c t d a , s , c , t L a
This is numerically identical to standard Y L L . The difference is interpretive: in the HCG framework, Y L L ( K ) measures the complete loss of the opportunity to exercise health-related freedoms over the foregone life course.

Appendix A.3. Estimating YLD (K)

Let:
P Y a , s , r , t
denote person-years lived in age group a , sex s , place r , and time period t .
Let:
p a , s , r , t , j
denote the prevalence of health state or sequela j in that population stratum.
Let:
D W j
denote the disability weight for health state j , where:
0 D W j 1
Then, for a single health state:
Y L D ( K ) a , s , r , t , j = P Y a , s , r , t p a , s , r , t , j D W j
Aggregating over population strata and health states:
Y L D ( K ) = a s r t j P Y a , s , r , t p a , s , r , t , j D W j
where individual-level data are available, let H i , j denote whether individual i experiences health state j :
H i , j { 0,1 }
For multiple overlapping health states, the combined disability weight may be estimated using the multiplicative function:
D W i * = 1 j = 1 J ( 1 H i , j D W j )
Then:
Y L D ( K ) = i P Y i D W i *
This is numerically equivalent to standard Y L D , but it is interpreted as partial health capability loss.

Appendix A.4. Estimating Raw Structural Capability Loss, Y L S ( K ) r a w

Let m = 1 , , M index structural deprivation states. These may include lack of essential healthcare access, food insecurity, unsafe water or sanitation, inadequate housing, climate vulnerability, displacement, or environmental insecurity. The manuscript treats these as structural capability-deprivation states rather than disease sequelae or ordinary GBD risk factors.
Let:
E a , s , r , t , m
denote exposure to deprivation state m in age group a , sex s , place r , and time period t . This may be represented either as an individual-level indicator:
E i , m { 0,1 }
or as a group-level prevalence:
0 E a , s , r , t , m 1
Let:
ω m
denote the Health Resource Constraint disutility weight for deprivation state m , where:
0 ω m 1
The weight ω m represents the average proportional reduction in health capability associated with structural deprivation m , before accounting for overlap with other structural constraints or with disease outcomes already captured in DALYs. These weights are analogous to disability weights, but they measure capability loss from structural deprivation rather than health loss from disease or injury.
For a single structural deprivation state:
Y L S ( K ) a , s , r , t , m r a w = P Y a , s , r , t E a , s , r , t , m ω m
For multiple overlapping deprivation states, define the combined raw structural disutility weight as:
ω a , s , r , t r a w = 1 m = 1 M 1 E a , s , r , t , m ω m
This multiplicative form ensures that the combined structural disutility remains bounded between 0 and 1, provided that each E a , s , r , t , m ω m lies between 0 and 1.
The raw structural capability loss is therefore:
Y L S ( K ) r a w = a s r t P Y a , s , r , t ω a , s , r , t r a w

Appendix A.5. Adjustment for Double-Counting

Structural deprivation may reduce health capability through two pathways.
First, it may directly reduce the real freedom to achieve health before or apart from any observed disease event:
Structural   deprivation Y L S ( K )
Second, it may indirectly produce morbidity or mortality already captured in DALYs:
Structural   deprivation disease   or   death Y L D ( K ) + Y L L ( K )
The paper’s limitations section explicitly notes that simply summing Y L S ( K ) and DALYs may double-count the same deprivation, because part of the structural constraint may already be reflected in Y L L ( K ) or Y L D ( K ) . It therefore requires the HRC component to be measured net of outcome effects already counted in DALYs.
Let:
θ a , s , r , t
denote the DALY-mediated share of raw structural capability loss in stratum a s r t . More precisely:
θ a , s , r , t = raw   structural   capability   loss   already   expressed   through   DALY - counted   morbidity   or   mortality total   raw   structural   capability   loss
with:
0 θ a , s , r , t 1
Then the double-counting-adjusted structural disutility weight is:
ω ~ a , s , r , t = ( 1 θ a , s , r , t ) ω a , s , r , t r a w
and the adjusted structural capability loss is:
Y L S ( K ) = a s r t P Y a , s , r , t ω ~ a , s , r , t
or:
Y L S ( K ) = a s r t P Y a , s , r , t ( 1 θ a , s , r , t ) ω a , s , r , t r a w
This adjustment preserves the interpretation of Y L S ( K ) as the component of structural capability loss not already realised as morbidity or mortality.
Where empirical data permit, θ a , s , r , t may be estimated using mediation analysis, multistate life-table methods, sequential g-estimation, inverse probability weighting, or related causal methods designed to separate the direct capability loss of deprivation from disease pathways already counted in DALYs. These approaches are consistent with the manuscript’s proposed treatment of the double-counting problem.

Appendix A.6. Health Capability Gap After Double-Counting Adjustment

Using the adjusted structural component:
H C G = Y L L ( K ) + Y L D ( K ) + Y L S ( K )
Since:
D A L Y = Y L L ( K ) + Y L D ( K )
it follows that:
H C G = D A L Y + Y L S ( K )
where:
Y L S ( K ) = a s r t P Y a , s , r , t ( 1 θ a , s , r , t ) ω a , s , r , t r a w
Thus:
H C G = D A L Y + a s r t P Y a , s , r , t ( 1 θ a , s , r , t ) ω a , s , r , t r a w
If:
Y L S ( K ) = 0
then:
H C G = D A L Y
If:
Y L S ( K ) > 0
then:
H C G > D A L Y
The latter condition applies when structural capability losses remain after excluding the portion already realised through DALY-counted morbidity or mortality.

Appendix A.7. Health-Adjusted Life Expectancy

Let:
l 0
denote the life-table radix, and let:
L a
denote life-table person-years lived in age interval a .
Let:
D W a
denote the average disability weight or average morbidity-related health loss in age interval a , with:
0 D W a 1
Then health-adjusted life expectancy may be written as:
H A L E = 1 l 0 a L a ( 1 D W a )
Total life expectancy is:
L E = 1 l 0 a L a
and the aggregate non-fatal health loss is:
Y L D ( K ) = a L a D W a
Therefore:
H A L E = L E Y L D ( K ) l 0

Appendix A.8. Additive Approximation for Capability-Adjusted Life Expectancy

Capability-Adjusted Life Expectancy, denoted C A L E , extends HALE by adjusting life expectancy not only for morbidity and disability but also for structural capability loss. The manuscript describes CALE as HALE further adjusted for Health Resource Constraint loss.
Under an additive approximation:
C A L E a d d = 1 l 0 a L a 1 D W a ω ~ a
where:
ω ~ a
is the age-specific double-counting-adjusted structural disutility weight.
This additive specification is valid subject to the constraint:
0 D W a + ω ~ a 1
for every age interval a . This condition ensures that adjusted person-years do not become negative.
Since:
Y L D ( K ) = a L a D W a
and:
Y L S ( K ) = a L a ω ~ a
the additive specification gives:
C A L E a d d = L E Y L D ( K ) l 0 Y L S ( K ) l 0
Because:
H A L E = L E Y L D ( K ) l 0
it follows that:
C A L E a d d = H A L E Y L S ( K ) l 0
This identity holds when Y L S ( K ) is defined over total life-table person-years and when the additive constraint above is satisfied.

Appendix A.9. Multiplicative Specification for Capability-Adjusted Life Expectancy

A multiplicative specification may be preferable where morbidity-related loss and structural capability loss overlap within the same person-years, because it preserves non-negative adjusted person-years without requiring the additive restriction D W a + ω ~ a 1 .
The multiplicative form is:
C A L E m u l t = 1 l 0 a L a ( 1 D W a ) ( 1 ω ~ a )
with:
0 D W a 1
and:
0 ω ~ a 1
This guarantees:
0 ( 1 D W a ) ( 1 ω ~ a ) 1
The multiplicative form can also be written as:
C A L E m u l t = 1 l 0 a H A L E P Y a ( 1 ω ~ a )
where:
H A L E P Y a = L a ( 1 D W a )
Under this formulation, the structural capability loss component embedded in CALE is:
Y L S ( K ) H A L E = a L a ( 1 D W a ) ω ~ a
Therefore:
C A L E m u l t = H A L E Y L S ( K ) H A L E l 0
This differs from the additive identity because the structural loss is applied to HALE-adjusted person-years rather than to total life-table person-years.

Appendix A.10. Equivalence Between HALE and CALE

Both additive and multiplicative specifications show that HALE and CALE are equivalent when structural capability loss is zero.
If:
ω ~ a = 0 a
then:
Y L S ( K ) = 0
Under the additive specification:
C A L E a d d = 1 l 0 a L a ( 1 D W a 0 )
so:
C A L E a d d = 1 l 0 a L a 1 D W a = H A L E
Under the multiplicative specification:
C A L E m u l t = 1 l 0 a L a ( 1 D W a ) ( 1 0 )
so:
C A L E m u l t = 1 l 0 a L a ( 1 D W a ) = H A L E
Therefore:
C A L E = H A L E
when:
Y L S ( K ) = 0
or equivalently:
ω ~ a = 0 a
More generally:
C A L E H A L E
with equality only when there is no double-counting-adjusted structural capability loss.

Appendix A.11. Summary of Core Identities

The central burden identity is:
D A L Y = Y L L + Y L D
The capability reinterpretation is:
Y L L K Y L L
Y L D K Y L D
Therefore:
D A L Y = Y L L ( K ) + Y L D ( K )
The Health Capability Gap is:
H C G = Y L L ( K ) + Y L D ( K ) + Y L S ( K )
or:
H C G = D A L Y + Y L S ( K )
where:
Y L S ( K ) = a s r t P Y a , s , r , t ( 1 θ a , s , r , t ) ω a , s , r , t r a w
and:
ω a , s , r , t r a w = 1 m = 1 M 1 E a , s , r , t , m ω m
The additive expectancy identity is:
C A L E a d d = H A L E Y L S ( K ) l 0
subject to:
0 D W a + ω ~ a 1
The multiplicative expectancy identity is:
C A L E m u l t = H A L E Y L S ( K ) H A L E l 0
where:
Y L S ( K ) H A L E = a L a ( 1 D W a ) ω ~ a
Finally:
C A L E = H A L E
when:
Y L S ( K ) = 0
and:
C A L E < H A L E
when positive double-counting-adjusted structural capability loss remains.

References

  1. Murray, C.J.L.; Lopez, A.D. The global burden of disease; Harvard University Press: Cambridge (MA), 1996. [Google Scholar]
  2. United Nations. Transforming our world: the 2030 Agenda for Sustainable Development; United Nations: New York, 2015. [Google Scholar]
  3. World Health Organization; World Bank. Tracking universal health coverage: 2023 global monitoring report; World Health Organization: Geneva, 2023. [Google Scholar]
  4. Sen, A. Development as freedom; Oxford University Press: Oxford, 1999. [Google Scholar]
  5. Watts, N.; Amann, M.; Arnell, N.; Ayeb-Karlsson, S.; Belesova, K.; Boykoff, M.; et al. The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate. Lancet 2019, 394(10211), 1836–78. [Google Scholar] [CrossRef] [PubMed]
  6. Salomon, J.A.; Haagsma, J.A.; Davis, A.; de Noordhout, C.M.; Polinder, S.; Havelaar, A.H.; et al. Disability weights for the Global Burden of Disease 2013 study. Lancet Glob. Health 2015, 3(11), e712-23. [Google Scholar] [CrossRef]
  7. Riumallo-Herl, C.; Canning, D.; Salomon, J.A. Measuring health and economic wellbeing in the Sustainable Development Goals era: development of a poverty-free life expectancy metric and estimates for 90 countries. Lancet Glob. Health 2018, 6(8), e843-58. [Google Scholar] [CrossRef] [PubMed]
  8. Baland, J.M.; Cassan, G.; Decerf, B. Poverty-adjusted life expectancy; World Bank: Washington (DC); Policy Research Working Paper No., 2022; p. 10133. [Google Scholar]
  9. World Health Organization. Constitution of the World Health Organization; World Health Organization: Geneva, 1946. [Google Scholar]
  10. Nielsen, L. A new theory of sufficientarian justice; Cambridge University Press: Cambridge, 2026. [Google Scholar]

Notes

[1]
This work was completed in March 2026, shared with colleagues for feedback, and subsequently submitted for potential publication to a relevant journal on 9 May 2026, where it remains under review. The current version was shared on a preprint platform on August 09, 2026.
[2]
The author was a member of the GBD Scientific Council (2016-2022), a task force member of the WHO Reference Group on Health Statistics (2021-2022), and a member of UNCTAD’s Statistical and Technical Advisory Group (2020-2024).
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